1,720,958 research outputs found
Sample characteristics stratified by gender.
No full text<p>Data presents the <i>n</i> and (percentage) unless stated otherwise.</p><p>Sample characteristics stratified by gender.</p
Prevalence of “Global Physical Activity Questionnaire” categories among the 18–65 year-old sample as well as the percentage of active participants meeting WHO guidelines.
No full text<p>Sample stratified by gender, age, BMI, residential setting, education and income level.</p><p>Data presents the percentage.</p><p>Prevalence of “Global Physical Activity Questionnaire” categories among the 18–65 year-old sample as well as the percentage of active participants meeting WHO guidelines.</p
Results from multiple linear regressions on contribution of socio-demographic correlates on the dependant variable “Overall MVPA”, “Work MVPA”, “Transport MVPA” and “Leisure MVPA”.
No full text<p>B = unstandardized beta; SE B = standard error of beta; β = standardized beta;</p><p>* = p<0.05;</p><p>** = p<0.01;</p><p>*** = p<0.001.</p><p>Results from multiple linear regressions on contribution of socio-demographic correlates on the dependant variable “Overall MVPA”, “Work MVPA”, “Transport MVPA” and “Leisure MVPA”.</p
MVPA MET-minutes•week<sup>−1</sup> in overall MVPA and in the domains work, transport and leisure. Sample is stratified by gender, age, BMI, residential setting, education and income.
No full text<p>Data presents the median and the (quartiles). Statistical difference was set by p<.05.</p>a<p>Age group 18–29 years differs significantly from age group 30–45 years.</p>b<p>Age group 18–29 years differs significantly from age group 46–65 years.</p>c<p>Participants with a BMI of <18.5-kg•m<sup>−2</sup> differ significantly from participants with BMI of 18.5–24.9 kg•m<sup>−2</sup>.</p>d<p>Participants with a BMI of 18.5–24.9 kg•m<sup>−2</sup> differ significantly from participants with BMI of 25.0–29.9 kg•m<sup>−2</sup>.</p>e<p>Participants with a BMI of 18.5–24.9 kg•m<sup>−2</sup> differ significantly from participants with BMI of>30 kg•m<sup>−2</sup>.</p>f<p>Participants living in areas with <5.000 inhabitants differ from participants living in areas 5.00–20.000 inhabitants.</p>g<p>Participants living in areas with <5.000 inhabitants differ from participants living in areas 20.00–100.000 inhabitants.</p>h<p>Participants living in areas with <5.000 inhabitants differ from participants living in areas 100.00–500.000 inhabitants.</p>i<p>Participants living in areas with <5.000 inhabitants differ from participants living in areas>500.000 inhabitants.</p>j<p>Participants living in areas with 5.000–20.000 inhabitants differ from participants living in areas 20.00–100.000 inhabitants.</p>k<p>Participants living in areas with 5.000–20.000 inhabitants differ from participants living in areas 100.000–500.000 inhabitants.</p>l<p>Participants living in areas with 5.000–20.000 inhabitants differ from participants living in areas>500.000 inhabitants.</p>m<p>People living in areas with 20.000–100.000 inhabitants differ from participants living in areas 100.00–500.000 inhabitants</p>n<p>Participants living in areas with 20.000–100.000 inhabitants differ from participants living in areas>500.000 inhabitants.</p>o<p>Participants with no graduation differ from participants with 10 years of education.</p>p<p>Participants with no graduation differ from participants with 12 years of education.</p>q<p>Participants with no graduation differ from participants with 13 years of education.</p>r<p>Participants with no graduation differ from participants with university degree.</p>s<p>Participants with 10 years of education differ from participants with 12 years of education.</p>t<p>Participants with 10 years of education differ from participants with 13 years of education.</p>u<p>Participants with 10 years of education differ from participants with university degree.</p>v<p>Participants with 12 years of education differ from participants with 13 years of education.</p>w<p>Participants with 12 years of education differ from participants with university degree.</p>x<p>Participants with 13 years of education differ from participants with university degree.</p>y<p>Participants with <1.500 € household net income/month differ from participants 1.500–2.999€ household net income/month.</p>z<p>Participants with <1.500 € household net income/month differ from participants>3.000€ household net income/month.</p>aa<p>Participants with 1.500–2.999 € household net income/month differ from participants>3.000€ household net income/month.</p><p>MVPA MET-minutes•week<sup>−1</sup> in overall MVPA and in the domains work, transport and leisure. Sample is stratified by gender, age, BMI, residential setting, education and income.</p
Additional file 1 of Type and intensity distribution of structured and incidental lifestyle physical activity of students and office workers: a retrospective content analysis
No full textAdditional file 1
Additional file 1 of Built and natural environment correlates of physical activity of adults living in rural areas: a systematic review
No full textSupplementary Material 1: Additional file 1: Search strategy [databases and search terms
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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